Search Results (64)

Radiative sky cooling can be effectively integrated with pipe-embedded wall systems to reduce building cooling loads. However, the energy-saving and carbon reduction potential of this technology varies according to climatic conditions and the method of integration, requiring quantification. To address this gap, a revised degree-hour method of evaluating energy efficiency for an integrated system is proposed and validated, and a global potential map is developed. The proposed method can be used to predict the energy-saving and carbon reduction potential of radiative sky coolers under different climatic conditions. Compared to physical model prediction methods, the revised degree-hour method is faster and more accurate, with an evaluation error of approximately 5%. The results indicate that the integrated system performs well in most regions with cooling demand. The system’s energy-saving potential is highest in cities in tropical savanna and desert climate zones, achieving energy savings of approximately 53.96 kWh/m² and reducing carbon emissions by approximately 22.99 kgCO₂/m² during the cooling season. Its performance is reduced in subtropical monsoon zones, with savings of 8.36 kWh/m² and 3.56 kgCO₂/m². Furthermore, the system’s energy-saving potential generally declines as the cold-water temperature of the radiative sky cooler increases, especially in tropical regions. This work provides a rapid assessment tool and global reference data to support low-energy building design. Full article

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Full article

Ulaanbaatar, the capital of Mongolia, operates one of the world’s largest district heating (DH) systems in the coldest national capital (heating degree-days ~5800). Despite serving over 60% of the city’s 1.6 million residents, the current 3rd generation DH system suffers from high thermal losses (~17–18%) and relies on coal-fired combined heat and power plants. Transitioning to 4th generation district heating (4GDH) with lower supply temperatures could reduce these losses while enabling future low-temperature renewable energy integration. A geographic information system (GIS)-based spatial heat load density (HLD) analysis uses operational data from the Ulaanbaatar District Heating Company, encompassing 13,500 buildings with a total connected capacity of 3924 MW. Grid-based spatial analysis was performed at two resolutions (1 km² and 2 km²). Threshold sensitivity analysis was conducted across HLD criteria of 1–5 MW/km². Results indicate that median HLD values exceed the European reference threshold of 3 MW/km², with log-normal distributions confirmed by Shapiro–Wilk tests. Three candidate pilot zones were identified. A hybrid temperature strategy (65/35 °C above −25 °C; 90/60 °C below) further contextualizes the findings. These results suggest spatially favorable conditions for 4GDH development, providing a quantitative foundation for subsequent techno-economic feasibility studies. Full article

The intensification of Global Warming and Urban Heat Island phenomena necessitates advanced, computationally effective tools for evaluating outdoor thermal comfort and microclimatic dynamics by means of Mean Radiant Temperature assessment. However, existing high-resolution physical models often suffer from prohibitive computational costs. This research proposes LUCIDiT (Lean Urban Comfort Intelligent Digital Twin), a physically based modeling framework implemented for a quick mean radiant temperature assessment inside complex urban morphologies. The method integrates a simplified balance of mutual radiative heat exchanges with recursive time-series filtering to account for the thermal inertia of different urban materials, alongside greenery heat exchange due to evapotranspiration. This architecture creates an operational urban comfort digital twin that reduces computational times by orders of magnitude for large-scale mappings, without sacrificing physical accuracy. Validation against drone-acquired thermographic data and the established Urban Multi-scale Environmental Predictor model demonstrates high reliability and coherence with the real physical phenomena and context. The application to an urban pilot site in Florence reveals that strategic interventions, such as substituting impervious surfaces with irrigated greenery and arboreal canopies, can mitigate radiant loads by up to 20 °C. Findings show that the proposed urban comfort digital twin can be a robust, scalable instrument for designing evidence-based climate adaptation strategies and quick testing mitigation scenarios to enhance urban resilience. Full article

Chlorophyll-a (Chl-a) is a critical indicator of freshwater ecosystem health, reflecting phytoplankton biomass and primary productivity. This study investigates the long-term dynamics of Chl-a concentrations in Chao Lake, China, over three decades (1993–2023), employing an integrated approach combining remote sensing, causality, and comprehensive land use and climate data analysis. Our findings reveal a dramatic 175% increase in Chl-a levels, from 37.26 km² (1.71%) in 1993 to 102.41 km² (4.71%) in 2023, highlighting the ongoing eutrophication crisis. Significant correlations were established between land cover changes and Chl-a dynamics, with built-up areas exhibiting a positive correlation of 0.763 with Chl-a. In contrast, vegetation cover showed an inverse correlation of −0.766. Rising land surface temperatures (LST) increased by 1.8 °C from 1993 to 2023, significantly affecting nutrient cycling and algal bloom proliferation. Precipitation trends indicate a decline of approximately 10% over the study period, further exacerbating hydrological stress and nutrient concentrations. Employing Convergent and Geographic Convergent cross-mapping, we established robust causal relationships, confirming that urbanization and climate variability are primary drivers of Chl-a fluctuations. These findings stress the urgent need for targeted management strategies to mitigate nutrient loading and improve water quality in Chao Lake. Full article

Mountain forests play a key role in buffering local climate, yet their climate sensitivity is seldom mapped in a way that is directly usable for spatial planning. This study investigates how phenological thermal and vegetation variability are organized within the forested landscape of Namyangju, a mountainous region in central Korea, and derives spatial indicators of forest climate sensitivity. Using monthly, cloud-screened Landsat-8/9 land surface temperature (LST) and normalized difference vegetation index (NDVI) images over a recent multi-year period, we calculated phenological coefficients of variation for 34,123 forest grid cells and applied local clustering analysis to identify belts of high and low variability. Forest areas where LST and NDVI variability simultaneously occupied the upper tail of their distributions (top 5%/10%/20%) were interpreted as climate-sensitivity hotspots, whereas co-located coldspots were treated as microclimatic refugia. Across the mountainous terrain, sensitivity hotspots formed continuous belts along high-elevation ridges and steep, dissected slopes, while coldspots were concentrated in sheltered valley floors. Notably, the most sensitive belts were dominated by high-elevation conifer stands, despite the limited seasonal fluctuation typically expected in evergreen canopies. This pattern suggests that elevation strongly amplifies the coupling between thermal responsiveness and vegetation health, whereas valley-bottom forests act as stabilizers that maintain comparatively constant microclimatic and phenological conditions. We refer to these patterns as “forest climate-sensitivity belts,” which translate satellite observations into spatially explicit information on where climate-buffering functions are most vulnerable or resilient. Incorporating climate-sensitivity belts into forest plans and adaptation strategies can guide elevation-aware species selection in new afforestation, targeted restoration and fuel-load management in upland sensitivity zones, and the protection of valley refugia that support biodiversity, thermal buffering, and hydrological regulation. Because the framework relies on standard satellite products and transparent calculations, it can be updated as new imagery becomes available and transferred to other seasonal, mountainous regions, providing a practical basis for climate-resilient forest planning. Full article

This study compared CO₂ emissions during a WLTP (Worldwide Harmonized Light-Duty Vehicles Test Procedure) test performed on a chassis dynamometer for the same flex-fuel vehicle, fuelled sequentially with E10 gasoline and E85 fuel. Based on the test data, a CO₂ emissions map was created, describing its dependence on speed and acceleration. The use of a 3D surface enabled the visualisation of the whole dynamics of emissions as a function of engine load in the WLTP cycle, including the identification of distinct emission peaks in areas of high positive acceleration. Analysis of the emission surface enabled the identification of structural differences between the fuels. For E85, more pronounced emission increases are observed in areas of intense acceleration, a consequence of the higher fuel demand resulting from the lower calorific value of bioethanol. In steady-state and moderate-load driving, CO₂ emissions for both fuels are similar. The results confirm that the main differences between E10 and E85 are not simply a shift in emission levels per se, but stem from variations in engine load during the dynamic cycle. Although E85 emits measurable CO₂ emissions, its carbon is not of fossil origin, highlighting the importance of biofuels in the context of greenhouse gas emission reduction strategies and the pursuit of climate neutrality. The presented methodology, combining chassis dynamometer tests with analysis of the speed-acceleration emission map, provides a tool for clearly identifying emission zones and can serve as a basis for further optimisation of engine control strategies and assessing the impact of fuel composition on emissions under dynamic conditions. Full article

Background: Butana are native Sudanese Bos indicus cattle that are well adapted to arid environments and valued for their relatively high milk performance and resilience under harsh conditions. Despite their adaptive advantages, Butana cattle face the risk of genetic erosion due to low production performance and the absence of structured breeding programs underscoring the urgent need to conserve their unique genetic potential for climate-resilient livestock development. Methods: In this study, we analyzed whole-genome sequencing data from 40 Butana cattle to assess their genetic diversity, population structure, signatures of selection, and potential pathogen load. Results: Butana cattle exhibited high nucleotide diversity and low levels of inbreeding, indicating a stable gene pool shaped by natural selection rather than by intensive breeding. Signatures of selection and functional variant analysis revealed candidate genes involved in heat stress adaptation (COL6A5, HSPA1L, TUBA8, XPOT), metabolic processes (G6PD, FAM3A, SLC10A3), and immune regulation (IKBKG, IRAK3, IL18RAP). Enrichment analyses and RoH island mapping consistently highlighted immune and thermoregulatory pathways as key selection targets, distinguishing Butana from both the geographically neighbored Kenana cattle and the specialized dairy cattle breed Holstein. Furthermore, metagenomic screening of unmapped reads detected the tick-borne parasite Theileria annulata and the opportunistic pathogen Burkholderia cenocepacia in all animals, underscoring the importance of integrating pathogen surveillance into genomic studies. Conclusions: Taken together, our findings highlight the distinct adaptive genomic profile of Butana cattle and reinforce their value in breeding programs aimed at improving climate resilience and disease resistance in livestock through the utilization of local breeds. Full article

Integrating phase change materials (PCMs) into buildings and HVAC systems improves thermal comfort and energy efficiency. This study presents a climate-responsive methodology for selecting optimal PCMs using a multi-criteria decision-making (MCDM) framework. AHP was employed to determine the relative importance of key thermophysical properties, including melting point (47.5%), latent heat of fusion (25.7%), volumetric latent heat (13.5%), thermal conductivity (6.8%), specific heat capacity (3.3%), and density (3.3%). These weights were applied across five MCDM techniques—COPRAS, VIKOR, TOPSIS, MOORA, and PROMETHEE II—to evaluate 16 PCM alternatives for three representative climate zones: temperate (18 °C), subtropical (23 °C), and tropical hot/desert (28 °C). The results consistently identified n-Heptadecane (C17) as the most suitable PCM for temperate and subtropical climates, while n-Octadecane (C18) and hydrated salts such as CaCl₂·6H₂O and Na₂CO₃·10H₂O were optimal for tropical zones. Results show that n-Heptadecane (C17) is optimal for temperate and subtropical zones (COPRAS K = 1.00; TOPSIS C = 0.79–0.82; PROMETHEE φ = 0.21–0.22), while n-Octadecane (C18) and hydrated salts such as CaCl₂·6H₂O and Na₂CO₃·10H₂O perform best in tropical climates (TOPSIS C = 0.85; PROMETHEE φ = 0.26). These PCMs offer high latent heat (up to 254 kJ·kg⁻¹) and volumetric storage (up to 381 MJ·m⁻³), enabling significant reductions in HVAC loads and improved indoor temperature stability. The convergence of rankings across methods and alignment with existing literature validate the robustness of the proposed approach. This framework supports informed material selection for sustainable building design and can be adapted to other climate-sensitive engineering applications. The framework introduces methodological innovations by explicitly mapping PCM melting points to climate-specific comfort bands, incorporating volumetric latent heat, and validating rankings through cross-method convergence (Spearman ρ > 0.99). Sensitivity analysis confirms robustness against weight perturbations. The approach supports practical PCM selection for both new and retrofit buildings, contributing to EU and US energy goals (e.g., 40% building energy use, DOE’s 50% reduction target). Full article

Reliable water vapor permeability (WVP) testing is crucial for sustainable construction, enabling accurate assessment of bio-mineral materials like hemp concrete, which reduce the environmental impact through renewable sourcing and improved energy efficiency. However, most studies testing or conditioning porous building materials in environmental chambers overlook the influence of chamber occupancy on airflow and humidity evacuation. While the usual practice is to collect anemometric velocity results in selected locations, few investigations apply computational fluid dynamics (CFD) to analyze the entire flow field, and humidity-field assessment is practically absent. This study addresses this gap by using CFD to examine how sample arrangement affects airflow and relative humidity (RH) in a climatic chamber containing sixteen hemp concrete specimens in dry- and wet-cup setups, aiding the reliable characterization of hygroscopic eco-composites. Three arrangements were modeled in ANSYS Fluent (2024 R1) using turbulence and species transport. Results show that unoptimized wet-cup placements cause RH deviations exceeding ISO’s ±5% tolerance, potentially biasing permeability data and undermining comparability across laboratories. A balanced wet–dry layout maintained RH within limits, improving testing reproducibility. Velocity maps reveal strong gradients above exposed sample surfaces, suggesting that standard anemometric protocols may require refinement. The presented approach highlights chamber loading as a hidden factor influencing WVP results and provides a transferable CFD-based framework to enhance testing accuracy, support sustainable material qualification, and accelerate the standardization of green-building methodologies. Full article

Oases are the core carriers of societal development in arid regions, and their spatial patterns have changed significantly, driven by climate change and anthropogenic activities. This study integrates historical documents, archeological materials, maps, and remote sensing data. The changes in the temperature, precipitation, settlements, war frequency, and oasis area were identified by combining quantitative and qualitative methods, and the partial least squares path model (PLS-PM) was utilized to quantify the natural and human driving factors. The results show that the oasis development in the Tarim and Heihe River Basins exhibits distinct spatio-temporal variability and phased characteristics and is comprehensively shaped by both natural and anthropogenic drivers. The Tarim Basin’s natural oases demonstrate a “fluctuating recovery” pattern. The cultivated oases gradually expanded. The natural oases within the Heihe River Basin have persistently decreased, and cultivated oases show a “U”-shaped evolution pattern. This reflects the strong intervention of human reclamation in the cultivated oases. The introverted social ecosystem has endowed the Tarim River Basin with the ability to self-repair and achieve a periodic recovery. The Heihe River Basin serves as a strategic corridor for national external engagement, relying on regime stability. A regime collapse led to its lack of a stable recovery period. The PLS-PM reveals that the Tarim River Basin oasis evolution is predominantly driven by climate fluctuations. The path coefficient of natural factors for artificial oases is 0.63, and extreme drought leads to natural oasis contraction. The human influence dominates the Heihe River Basin, with a −0.93 path coefficient linking the cultivated oasis area to human factors. The frequency of wars (load 0.74) and changes in settlements (load −0.92) are the key factors. This study provides a powerful case for the analysis of the evolution and driving mechanism of future oases in drylands. Full article

With the growing global focus on sustainable development and climate change mitigation, promoting the low carbonization of energy systems has become an inevitable trend. Power load monitoring is crucial to achieving efficient power management, and load identification is the key link. The traditional load identification method has the problem of low accuracy. It is assumed that the technique of fusing harmonic features through color coding can improve the accuracy of load identification. In this paper, the load’s instantaneous reactive power, power factor and current sequence distribution characteristics are used as the mapping characteristics of the R, G and B channels of the two-dimensional V–I trajectory color image of the load using color coding technology. The harmonic amplitude characteristics are integrated to construct the mixed-color image of the load. The void residual shrinkage neural network is selected as the classification training model. The advantages and disadvantages of two residual shrinkage construction units, RSBU-CS and RSBU-CW, are analyzed. A single-load identification model with three RSBU-CWs is built. Different datasets verify the performance of the model. Compared with the test results of the ordinary color image dataset, the accuracy of the mixed-color image dataset is above 98%, and the accuracy of load identification is improved. Full article

Moisture ingress is a critical concern in buildings, as it may profoundly affect structural integrity, the energy efficiency of a building, and as well the quality of the indoor environment that, in turn, could influence the health and safety of building occupants. Moisture ingress can occur during any phase in the lifecycle of a building component, where environmental loads, such as precipitation, wind, snow, and elevated relative humidity, play a fundamental role in affecting the building structure. Climate change exacerbates the issue of moisture ingress by intensifying these loads. In this review paper, the statistical perspective on publications related to moisture ingress in building envelope materials (BEMs) was first assessed through a scientometric study. All relevant publications were gathered and manually filtered, and the selected papers were categorized based on the topics discussed. The results of the scientometric study, as presented in this paper, include a bar chart in which the number of publications in each category is illustrated; a science journal mapping diagram showing the interdisciplinary connections of the research; a cluster map depicting the network between topics; and an R&D momentum analysis reflecting the rate of growth and publication count in this field. Given the strong focus on material properties, this review also examines experimental methods for characterizing moisture transport properties in building materials used in BEMs. Additionally, the differences between various codes and standards centered on this topic are reviewed and discussed. This combined strategy is intended to comprehensively evaluate available information and approaches to permit identifying the knowledge gaps that need to be addressed. Full article

Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works. Full article

Seagrass meadows, including those formed by Zostera noltei, play a crucial role in marine ecosystem health by providing habitat stability and coastal protection. In the Romanian Black Sea, Z. noltei meadows are critically endangered due to pressures from eutrophication, habitat loss, and climate change. This study presents a comprehensive baseline assessment of Z. noltei meadows near Mangalia, Romania, utilizing in situ field methods and UAV mapping conducted in the spring and summer of 2023. Seven meadow sites (Z1–Z7) were identified, with notable variability in density, shoot counts, and coverage across sites. Site Z1 exhibited the highest density (1223 shoots/m⁻²) and Z5 and Z7 the longest leaves (an average of 60 cm), reflecting possible environmental influences. Statistical analyses revealed significant inter-site differences in shoot density and leaf length, with density emerging as a primary differentiator. Ex situ analyses of epiphyte load indicated a median, balanced epiphyte load. This baseline dataset supported the selection of Z1 as a reference donor site for seagrass relocation activities along the Romanian coast in 2023. By providing critical insights into Z. noltei structure and health, this study supports future conservation efforts and evidence-based management of these vulnerable coastal habitats. Full article